@conference {1510357,
	title = {Monitoring Deployed Agent Teams },
	booktitle = {International Conference on Autonomous Agents (Agents{\textquoteright}01)},
	year = {2001},
	abstract = {Recent years have seen an increasing need for on-line monitoring
of deployed distributed teams of cooperating agents, for visualization, for performance tracking, etc. However, in deployed applications, we often cannot rely on the agents communicating their state
to the monitoring system: (a) we rarely have the ability to change
the behavior of already-deployed agents such that they communicate the required information (e.g., in legacy or proprietary systems); (b) different monitoring goals require different information
to be communicated (e.g., agents{\textquoteright} beliefs vs. plans); and (c) communications may be expensive, unreliable, or insecure. This paper presents a non-intrusive approach based on plan-recognition, in
which the monitored agents{\textquoteright} state is inferred from observations of
their normal course of actions. In particular, we focus on inference
of the team state based on its observed routine communications, exchanged as part of coordinated task execution. The paper includes
the following key novel contributions: (i) a linear time probabilistic plan-recognition algorithm, particularly well-suited for processing communications as observations; (ii) an approach to exploiting
general knowledge of teamwork to predict agent responses during
normal and failing execution, to reduce monitoring uncertainty; and
(iii) a technique for trading expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any
number of agents and their different activities, to be represented in
a single coherent entity. Our empirical evaluation illustrates that
monitoring based on observed routine communications enables significant monitoring accuracy, while not being intrusive. The results
also demonstrate a key lesson: A combination of complementary
low-quality techniques is cheaper, and better, than a single, highly optimized monitoring approach.},
	author = {Gal Kaminka and D. V. Pynadath and Tambe, Milind}
}
